Automated risk visualization using customer-centric data analysis

US11216762B1 · kind B1 · utility

Assignee

• Palantir Technologies Inc. · US

Inventors

• Alexander Maass · Ludwigsburg, DE
• Ben Regev · Holon, IL
• Duncan Hoffman · Paris, FR
• Eugene Mak · Sydney, AU
• Elise Norman · London, GB
• Elizabeth Patitucci · London, GB
• Yevhen Shevchuk · Horlivka, UA
• Harkirat Singh · Santa Clara, US
• Joshua Aschheim · New York, US
• Juan Jimenez Puig · Amsterdam, NL
• Jorien van den Bergh · London, GB
• Kai Kamberger · London, GB
• Maciej Biskupiak · Longridge, GB
• Marissa Miracolo · New York, US
• Matthew Julius Wilson · London, GB
• Nicolas Prettejohn · Bath, GB
• Patrick Walter · Berlin, DE
• Rootul Patel · New York, US
• Stephen Heitkamp · London, GB
• Richard Deitch · Dallas, US

Key dates

• Filing date: Aug 23, 2017
• Grant date: Jan 4, 2022
• Expiry date: Oct 6, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q20/10
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.